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    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Supervised Fine Tuning of StarCoder2-3B for Text to Cypher Generation"
      ],
      "metadata": {
        "id": "VdobwAPSIioM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Sources:**  \n",
        "- [Phil Schmid, How to Fine-Tune LLMs in 2024 with Hugging Face](https://www.philschmid.de/fine-tune-llms-in-2024-with-trl)  \n",
        "- [Sebastian Raschka, Practical Tips for Finetuning LLMs using LoRA](https://magazine.sebastianraschka.com/p/practical-tips-for-finetuning-llms)\n",
        "- [Sebastian Raschka, Finetuning LLMs with LoRA and QLoRA: Insights from Hundreds of Experiments](https://lightning.ai/pages/community/lora-insights/)\n",
        "-[Hugging Face - The Alignment Notebook](https://github.com/huggingface/alignment-handbook/tree/main)"
      ],
      "metadata": {
        "id": "RLenJv71igk6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Workspace Setup"
      ],
      "metadata": {
        "id": "tz7A1c_Vi2NY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The setup as of May 2024, it will probably change in time. This notebook is run in Google Colab Pro with A100 GPU and High-RAM setting."
      ],
      "metadata": {
        "id": "BB8SbgCp9Wz6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Necessary Installs\n",
        "\n",
        "!pip install -U accelerate\n",
        "!pip install -U bitsandbytes\n",
        "!pip install -U datasets\n",
        "!pip install -U einops\n",
        "!pip install -U evaluate\n",
        "!pip install -U ninja\n",
        "!pip install -U packaging\n",
        "!pip install -U peft\n",
        "!pip install -U tensorboard\n",
        "!pip install -U torch\n",
        "!pip install -U transformers\n",
        "!pip install -U trl"
      ],
      "metadata": {
        "id": "mn-XY1_Fx9Ru"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Required Imports\n",
        "\n",
        "# Python packages\n",
        "import json\n",
        "import gc\n",
        "\n",
        "# For Google Colab settings\n",
        "from google.colab import userdata, drive\n",
        "\n",
        "# For Hugging Face Hub setting\n",
        "from huggingface_hub import login\n",
        "\n",
        "# To work with data in Hugging Face format\n",
        "import datasets\n",
        "from datasets import load_dataset, Dataset\n",
        "\n",
        "import torch\n",
        "\n",
        "# To parse the data with chat_template\n",
        "from multiprocessing import cpu_count\n",
        "\n",
        "#Imports for model, tokenizer, training\n",
        "import transformers\n",
        "from transformers import (AutoTokenizer,\n",
        "                          AutoModelForCausalLM,\n",
        "                          BitsAndBytesConfig,\n",
        "                          TrainingArguments,\n",
        "                          pipeline\n",
        "                          )\n",
        "\n",
        "from peft import (LoraConfig,\n",
        "                  PeftConfig,\n",
        "                  PeftModel,\n",
        "                  get_peft_model,\n",
        "                  prepare_model_for_kbit_training,\n",
        "                  AutoPeftModelForCausalLM\n",
        "                  )\n",
        "import trl\n",
        "from trl import (SFTTrainer,\n",
        "                 setup_chat_format,\n",
        "                 )"
      ],
      "metadata": {
        "id": "2VzDxCqWLjKN"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Display Relevant Libraries Versions\n",
        "\n",
        "print(f\"The PyTorch version is {torch.__version__}.\")\n",
        "print(f\"Datasets version is {datasets.__version__}.\")\n",
        "print(f\"Transformers version is {transformers.__version__}.\")\n",
        "print(f\"TRL version is {trl.__version__}.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uEx9TPwDhIZf",
        "outputId": "fc26cd54-4b12-4783-fa61-5956fae6c169"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The PyTorch version is 2.3.0+cu121.\n",
            "Datasets version is 2.19.1.\n",
            "Transformers version is 4.41.0.\n",
            "TRL version is 0.8.6.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Assert Cuda Capability for Flash Attention\n",
        "\n",
        "major_version, minor_version = torch.cuda.get_device_capability()\n",
        "print(f\"Cuda major version: {major_version}.\\nCuda minor version: {minor_version}\")\n",
        "assert major_version >= 8, \"Hardware not supported by Flash Attention.\""
      ],
      "metadata": {
        "id": "l5I-vyOeA0Jc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9f50d39e-ea5b-46eb-bf8c-e39e288a9062"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cuda major version: 8.\n",
            "Cuda minor version: 0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Install Flash Attention\n",
        "\n",
        "# Limit the number of jobs to accomodate the compute capabilities\n",
        "%env MAX_JOBS=2 # for Google Colab\n",
        "\n",
        "# Install flash attention - for Ampere GPUs\n",
        "!pip install -U flash-attn --no-build-isolation"
      ],
      "metadata": {
        "id": "2mjQRfdVohR5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Resources Estimation\n",
        "gpu_stats = torch.cuda.get_device_properties(0)\n",
        "start_gpu_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\n",
        "max_memory = round(gpu_stats.total_memory / 1024 / 1024 / 1024, 3)\n",
        "print(f\"GPU = {gpu_stats.name}. Max memory = {max_memory} GB.\")\n",
        "print(f\"{start_gpu_memory} GB of memory reserved.\")"
      ],
      "metadata": {
        "id": "pygjKxpqLbRF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a9f08b21-b744-43d5-861f-dbee45dc43ca"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "GPU = NVIDIA A100-SXM4-40GB. Max memory = 39.564 GB.\n",
            "0.0 GB of memory reserved.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Hugging Face Credentials\n",
        "\n",
        "# Upload the HuggingFace token (should have WRITE access) from Colab secrets\n",
        "HF = userdata.get('HF')\n",
        "\n",
        "# This is needed to upload the model to HuggingFace\n",
        "login(token=HF,add_to_git_credential=True)"
      ],
      "metadata": {
        "id": "3jGammx2o9Kg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5214c63c-96dc-4df2-eeb9-40393df0396e"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Token is valid (permission: write).\n",
            "Your token has been saved in your configured git credential helpers (store).\n",
            "Your token has been saved to /root/.cache/huggingface/token\n",
            "Login successful\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Google Colab Drive Helper\n",
        "\n",
        "# This will prompt for authorization\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "# Set the working directory\n",
        "%cd '/content/drive/MyDrive/finetuneCypher/'"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_bnmZPWB0XHN",
        "outputId": "60b91558-ef3b-4efb-c309-9834c19b8af3"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n",
            "/content/drive/MyDrive/finetuneCypher\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Path Variables\n",
        "\n",
        "# Create a path variable for the data folder\n",
        "data_path = '/content/drive/MyDrive/finetuneCypher/datas/'\n",
        "\n",
        "# Supervised fine-tuning dataset\n",
        "trainer_with_repeats_file = 'parametric_trainer_with_repeats.json'\n",
        "\n",
        "# Create a path variable for the SFT model to be saved locally\n",
        "model_path = '/content/drive/MyDrive/finetuneCypher/cypherStarCoder2/'"
      ],
      "metadata": {
        "id": "j88Xb-l80nLX"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title The Model Name\n",
        "\n",
        "model_id =  \"bigcode/starcoder2-3b\""
      ],
      "metadata": {
        "id": "9SsbUAt34sGm"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Parse the Dataset with Chat Template"
      ],
      "metadata": {
        "id": "jnXQNWeLDEOY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Load the SFT Dataset\n",
        "\n",
        "with open(data_path+trainer_with_repeats_file, 'rb') as f:\n",
        "\tsampler = json.load(f)\n",
        "\n",
        "# Display an entry\n",
        "sampler[123]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z9jcCWu7he9I",
        "outputId": "479949d4-ef73-4da1-fcb2-774db6d901ab"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Prompt': 'Convert the following question into a Cypher query using the provided graph schema!',\n",
              " 'Question': 'Fetch the Author nodes and extract their affiliation property!',\n",
              " 'Schema': 'Graph schema: Relevant node labels and their properties (with datatypes) are:\\nAuthor {affiliation: STRING}',\n",
              " 'Cypher': 'MATCH (n:Author) RETURN n.affiliation'}"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Parse to Conversational Format\n",
        "\n",
        "system_message = \"\"\"\n",
        "You are a text to Cypher query translator. {prompt}\\n{schema}\n",
        "\"\"\"\n",
        "\n",
        "# Function to transform the data to conversational format {role:, content: }\n",
        "def create_conversation(sample):\n",
        "    return {\n",
        "        \"messages\": [\n",
        "            {\"role\": \"system\",\"content\": system_message.format(prompt=sample[\"Prompt\"], schema=sample[\"Schema\"])},\n",
        "            {\"role\": \"user\", \"content\": sample[\"Question\"]},\n",
        "            {\"role\": \"assistant\", \"content\": sample[\"Cypher\"]}\n",
        "        ]\n",
        "    }\n"
      ],
      "metadata": {
        "id": "Fh4YK9TEhuAj"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Convert Data to HuggingFace Format\n",
        "\n",
        "from datasets import load_dataset, Dataset\n",
        "\n",
        "dataset = Dataset.from_list(sampler)\n",
        "dataset = dataset.shuffle()\n",
        "\n",
        "# Transform to required format\n",
        "dataset = dataset.map(create_conversation,\n",
        "                      remove_columns=dataset.features,\n",
        "                      batched=False)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "054a25dafef0404e895245322871445f",
            "a14bd48da60e42d1aab5dba7af65243d",
            "2c9ef2aea7c24834b4f8f20c305dbfdb",
            "4fcc3ad011a34e4abf85babc8dc55760",
            "feaf3cb0f70a489990b04537810fc3c6",
            "0daf2cd0e9554eb9a8b099ec93410741",
            "59bd7fd4913e4ed49f87bff523840ad8",
            "0837369a27324e5389edb75beb53496a",
            "b9f9740caf9e45b9ba9681df769c922c",
            "e5ef8027b85b49ec915056863be34feb",
            "198b3eac32d64fd59f91035428763a88"
          ]
        },
        "id": "H_I-NKMK5BGk",
        "outputId": "c4a2e5e4-41d7-4b42-f8e1-645cf955f3e1"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/30116 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "054a25dafef0404e895245322871445f"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Split Data into Train and Test Sets\n",
        "dataset = dataset.train_test_split(test_size=0.1, seed=23)\n",
        "\n",
        "print(dataset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8EHbMYzlqV-3",
        "outputId": "1b1d3f71-2d96-4dcc-c448-0d99e46df525"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DatasetDict({\n",
            "    train: Dataset({\n",
            "        features: ['messages'],\n",
            "        num_rows: 27104\n",
            "    })\n",
            "    test: Dataset({\n",
            "        features: ['messages'],\n",
            "        num_rows: 3012\n",
            "    })\n",
            "})\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Display a Sample\n",
        "dataset[\"train\"][32][\"messages\"]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fv0BDo1teZGj",
        "outputId": "db5c3529-af11-4b41-8fe0-0ff773d4e70f"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'content': '\\nYou are a text to Cypher query translator. Convert the following question into a Cypher query using the provided graph schema!\\nGraph schema: Relevant node labels and their properties (with datatypes) are:\\nDOI {name: STRING}\\n',\n",
              "  'role': 'system'},\n",
              " {'content': 'Find 10 DOI that have the name recorded and return these values!',\n",
              "  'role': 'user'},\n",
              " {'content': 'MATCH (n:DOI) WHERE n.name IS NOT NULL RETURN n.name LIMIT 10',\n",
              "  'role': 'assistant'}]"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Save the Dataset Splits\n",
        "dataset[\"train\"].to_json(data_path+\"train_dataset.json\", orient=\"records\")\n",
        "dataset[\"test\"].to_json(data_path+\"test_dataset.json\", orient=\"records\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 98,
          "referenced_widgets": [
            "b90758726f8441179a09c6830207fe21",
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            "934745afa03e493b8e127aa25691eaa4",
            "d1b936d04c0447f5890f655929c4355d",
            "79066f44eb384abb94771a29a394dd7a",
            "d902754923924c78aefa28fba90101af",
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            "575a3463625a4b27ae2b82a97d9c4c67",
            "16c00bb3ca364d45b550b75b2e12bb53",
            "77a77db4e7464330ae234589b7976c5d",
            "b3e1f5997ee04037b8ec3b7753aec18f",
            "2d8b55d8583a492b8effe9501a9c41b6",
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            "0a7da594a6d64be599bd904e91380378",
            "add75d2101414942b9a79de0f393bd48",
            "99a253d31bf6409c853e3e9b8ae38572",
            "3fb4750ff4b84b8b84e03e24bc7acac4",
            "5afdd2a2bfe248c8ae177bc918f17bd1",
            "75247ab6d33d4bbd8885b4571aee1949",
            "3e46e8c589034863bc772a255a167ad7",
            "6fe63c096c394b5fa1679fd64e791d9f"
          ]
        },
        "id": "DYL9FUt8-dUK",
        "outputId": "585a89d9-9cf2-472d-faa8-0d1907efa8a1"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Creating json from Arrow format:   0%|          | 0/28 [00:00<?, ?ba/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b90758726f8441179a09c6830207fe21"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Creating json from Arrow format:   0%|          | 0/4 [00:00<?, ?ba/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b3e1f5997ee04037b8ec3b7753aec18f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2184680"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Tokenizer Settings"
      ],
      "metadata": {
        "id": "sPEWI0cMa-Hu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Load Tokenizer\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
        "\n",
        "tokenizer.pad_token_id = tokenizer.eos_token_id\n",
        "tokenizer.padding_side = 'right' # added to prevent warnings\n",
        "\n",
        "# Set a maximum length\n",
        "tokenizer.model_max_length = 2048"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301,
          "referenced_widgets": [
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            "2a66fed299f143bb82966130ce3b7306",
            "48e24abd9f504e55958f3a48aede4240",
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            "294c7a5459c04737bc2303d0b0a8ae3b",
            "807ce505691d4c069e2aa7b3cb9ce7e6",
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            "f6cef15474ce469a827dfb8f892b7b3c",
            "60cfe09a3ac3414f9585a87de25292e0",
            "94cfada90a3d4189a96f4b648ccd0132",
            "45eac955fc214b2a9cea7ae8287bb78b",
            "4c697e9572ca4f4f92c99f77dbfe00c0",
            "bd29bec8cbc840bd957333439078a62c",
            "65c63deb24b8476cac7215060e806bae",
            "fafd47ac4b0740e5a16d970aeca97bb4",
            "0abb40b40fe0422e8bc37e79f89b209a",
            "c9f2ce4c919b487c840e95e0c397383e",
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            "4fc2c02e74a34c9aa460c2e3e2dc96cd",
            "1937a29a4eac46ecbc7b99b82a2f9649"
          ]
        },
        "id": "NeyEtOQCbEle",
        "outputId": "360cd6be-f5b1-48bf-b41d-8e9c63914165"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/7.88k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "50bcd256c9b441f1b54255257afdb88a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.json:   0%|          | 0.00/777k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ca5faf1a703b4705a958568d3be21ac4"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "merges.txt:   0%|          | 0.00/442k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/2.06M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2a66fed299f143bb82966130ce3b7306"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/958 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "94cfada90a3d4189a96f4b648ccd0132"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title  Define a Chat Template\n",
        "\n",
        "CHAT_TEMPLATE = \"{% for message in messages %}\\n{% if message['role'] == 'user' %}\\n{{ '<|im_start|>user\\n' + message['content'] + '<|im_end|>'+eos_token }}\\n{% elif message['role'] == 'system' %}\\n{{ '<|im_start|>system\\n' + message['content'] + '<|im_end|>'+eos_token }}\\n{% elif message['role'] == 'assistant' %}\\n{{ '<|im_start|>assistant\\n'  + message['content'] + '<|im_end|>'+eos_token }}\\n{% endif %}\\n{% if loop.last and add_generation_prompt %}\\n{{ '<|im_start|>assistant' }}\\n{% endif %}\\n{% endfor %}\"\n",
        "\n",
        "tokenizer.chat_template = CHAT_TEMPLATE"
      ],
      "metadata": {
        "id": "OxzCYFLBb1_X"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Reload Dataset if Necessary\n",
        "\n",
        "train_dataset = load_dataset(\"json\",\n",
        "                       data_files=data_path+\"train_dataset.json\",\n",
        "                       split=\"train\")"
      ],
      "metadata": {
        "id": "KMabEVXgejWz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "6143a0b540cd478e99da73769980b88a",
            "d8c12c1230e2403aac96f35d316dcb7c",
            "3fc39cfa2efe4ea6b2cf1e5014726e1f",
            "d0d387574cae4b71872bdc67cbd83bf9",
            "dc2e1b8c73c7457a87c2600ed9a44a92",
            "cd82efddd1444c9697aa5f8ac722be50",
            "dd1d01aecfa8452f8e1c19096c01a35c",
            "6dc65f9056464b439d2995a8197f247b",
            "38e16755b0ad4886a227e6c9dfda5d9d",
            "26c067ec37b94957acda3f587290487b",
            "21915a6215a44c0ebc63e423a7aaf953"
          ]
        },
        "outputId": "dc2f491d-3f8d-4616-f7a3-0096a05feca2"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6143a0b540cd478e99da73769980b88a"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Apply the Chat Template\n",
        "\n",
        "# Process the dataset function\n",
        "def apply_chat_template(sample, tokenizer):\n",
        "    messages = sample[\"messages\"]\n",
        "\n",
        "    # We add an empty system message if there is none\n",
        "    if messages[0][\"role\"] != \"system\":\n",
        "        messages.insert(0, {\"role\": \"system\", \"content\": \"\"})\n",
        "\n",
        "    sample[\"text\"] = tokenizer.apply_chat_template(messages,\n",
        "                                                   tokenize=False)\n",
        "\n",
        "    return sample\n",
        "\n",
        "# Apply the chat template to the entire dataset\n",
        "train_dataset = train_dataset.map(apply_chat_template,\n",
        "                      fn_kwargs={\"tokenizer\": tokenizer},\n",
        "                      remove_columns=train_dataset.features,\n",
        "                      )\n",
        "print(train_dataset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118,
          "referenced_widgets": [
            "c553376d11a14776b0ac2463d223b9d2",
            "fa5a3295746044eda2d5d6294581c064",
            "f50590a6d29f4e43b179c258661ebc4b",
            "59d0c981931645cd9d0439eecfc13a29",
            "6575b846ec8549d98646127c9b35120d",
            "5e481e4a2a0348888e1b0fc59429a3f6",
            "422728fd880e491caf2c3d0af63303fe",
            "2fa43766df6345cf921c2766b5312d06",
            "7b62d1b72e5140cfb9c52d59fa86cf44",
            "4ec6cb297bd64f2ca855ad24340a8ae4",
            "b3602669fad745fcb40f9c016356e71b"
          ]
        },
        "id": "rmI8snXacQHU",
        "outputId": "131723ef-8784-4b32-d2b0-a09d7751472f"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/27104 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c553376d11a14776b0ac2463d223b9d2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset({\n",
            "    features: ['text'],\n",
            "    num_rows: 27104\n",
            "})\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Display a Sample\n",
        "print(train_dataset[123][\"text\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7K_JhxFLchS1",
        "outputId": "3ade238e-053f-4615-9365-8058dd6be434"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<|im_start|>system\n",
            "\n",
            "You are a text to Cypher query translator. Convert the following question into a Cypher query using the provided graph schema!\n",
            "Relevant node labels and their properties (with datatypes) are:\n",
            "Article {comments: STRING}\n",
            "Journal {name: STRING}\n",
            "\n",
            "Relevant relationships are:\n",
            "{'start': Article, 'type': PUBLISHED_IN, 'end': Journal }\n",
            "\n",
            "\n",
            "Relevant relationship properties (with datatypes) are:\n",
            "PUBLISHED_IN {meta: STRING}\n",
            "<|im_end|><|endoftext|>\n",
            "<|im_start|>user\n",
            "Calculate the average name for Journal that is linked to Article via PUBLISHED_IN where meta is 220 and has comments date before December 31, 2020!<|im_end|><|endoftext|>\n",
            "<|im_start|>assistant\n",
            "MATCH (n:Article) -[:PUBLISHED_IN{meta: '220'}]->(m:Journal) WHERE m.comments < date('2020-12-31') RETURN avg(m.name) AS avg_name<|im_end|><|endoftext|>\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Load Base Model"
      ],
      "metadata": {
        "id": "eCItLkz-FCyj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Quantization Parameters\n",
        "\n",
        "bnb_config = BitsAndBytesConfig(\n",
        "    load_in_4bit=True,\n",
        "    bnb_4bit_use_double_quant=True,\n",
        "    bnb_4bit_quant_type=\"nf4\",\n",
        "    bnb_4bit_compute_dtype=torch.bfloat16   # change to float16 if using non-Ampere GPU\n",
        ")"
      ],
      "metadata": {
        "id": "bFAtBa3dUW4k"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Device Map\n",
        "\n",
        "device_map = {\"\": torch.cuda.current_device()} if torch.cuda.is_available() else None"
      ],
      "metadata": {
        "id": "uitS80xVct3q"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Load the Model in 4-bit\n",
        "\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "    model_id,\n",
        "    device_map=device_map, #device_map,\n",
        "    attn_implementation=\"flash_attention_2\", # remove if using non-Ampere GPU\n",
        "    torch_dtype=torch.bfloat16, #bfloat16, # change to float16 if using non-Ampere GPU\n",
        "    quantization_config=bnb_config\n",
        ")\n",
        "\n",
        "model.config.use_cache = False"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
            "0095c50e20d84b44872c13f7c9c88e3f",
            "c2f4410a70e24baba3835c7c868a669c",
            "a21abbe18e0b4fb58e131cfa72e2da9d",
            "961762f52d2440749c27a78f99d6e9f6",
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            "4f39e72d175d45909bc6a18a96cdae63",
            "b0d7b246661d4aca8b4572e9b17e8e4e",
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            "f9cf52947541463d80524b9a30377433",
            "972c89267da0418e8fd89171f647b293",
            "f3f0c1d6aba8433095e9b397f6f97121",
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            "53f3962eee134dd3af13d2fc6699d025",
            "9e8fd98d3dff456987752254ede32b84",
            "9edda18317924383aba409b252fc7e4d",
            "dc5291a57b5642159af67f8e9806c8e5",
            "765a8c5221164435ae0f265f928335ec",
            "1d3efc7f139b4389bba8c94cfb423ec1",
            "e5b861263fd94a299adecdc180ef2f0b",
            "44cd7f5e85b74a18a6d0fe957cbf2777",
            "514ded5429fb4f4f99de3b4916dd8285",
            "02d2ca52dda04ba68fcd933eaf34c8ae"
          ]
        },
        "id": "rKJXX4qE53MQ",
        "outputId": "70c44bd3-28e8-4d87-9233-3101d38f583f"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/700 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0095c50e20d84b44872c13f7c9c88e3f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/12.1G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "46c54ac2acca4156b0156a7cd8b24e34"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## SFT Train Model and Save"
      ],
      "metadata": {
        "id": "5nWFCpxYdmdd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Prepare Model for Training\n",
        "\n",
        "model = prepare_model_for_kbit_training(model)"
      ],
      "metadata": {
        "id": "1MECUbXCdtVG"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Identify the Linear Layers\n",
        "\n",
        "print(model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BmEqIxzOMbyI",
        "outputId": "a7ebe76e-8105-4a76-b075-c181d6c50917"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Starcoder2ForCausalLM(\n",
            "  (model): Starcoder2Model(\n",
            "    (embed_tokens): Embedding(49152, 3072)\n",
            "    (layers): ModuleList(\n",
            "      (0-29): 30 x Starcoder2DecoderLayer(\n",
            "        (self_attn): Starcoder2FlashAttention2(\n",
            "          (q_proj): Linear4bit(in_features=3072, out_features=3072, bias=True)\n",
            "          (k_proj): Linear4bit(in_features=3072, out_features=256, bias=True)\n",
            "          (v_proj): Linear4bit(in_features=3072, out_features=256, bias=True)\n",
            "          (o_proj): Linear4bit(in_features=3072, out_features=3072, bias=True)\n",
            "          (rotary_emb): Starcoder2RotaryEmbedding()\n",
            "        )\n",
            "        (mlp): Starcoder2MLP(\n",
            "          (c_fc): Linear4bit(in_features=3072, out_features=12288, bias=True)\n",
            "          (c_proj): Linear4bit(in_features=12288, out_features=3072, bias=True)\n",
            "          (act): PytorchGELUTanh()\n",
            "        )\n",
            "        (input_layernorm): LayerNorm((3072,), eps=1e-05, elementwise_affine=True)\n",
            "        (post_attention_layernorm): LayerNorm((3072,), eps=1e-05, elementwise_affine=True)\n",
            "      )\n",
            "    )\n",
            "    (norm): LayerNorm((3072,), eps=1e-05, elementwise_affine=True)\n",
            "  )\n",
            "  (lm_head): Linear(in_features=3072, out_features=49152, bias=False)\n",
            ")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title LoRA Configuration\n",
        "\n",
        "# According to Sebastian Raschka findings\n",
        "peft_config = LoraConfig(\n",
        "        lora_alpha=128,\n",
        "        lora_dropout=0.05,\n",
        "        r=256,\n",
        "        bias=\"none\",\n",
        "        target_modules=[\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\", \"c_fc\", \"c_proj\"], # use all linear layers\n",
        "        task_type=\"CAUSAL_LM\",\n",
        ")"
      ],
      "metadata": {
        "id": "xnbLWOYNFFoF"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @title SFT Training Arguments\n",
        "\n",
        "# Adapted from  Phil Schmid blogpost\n",
        "args = TrainingArguments(\n",
        "    output_dir=model_path,                  # directory to save the model and repository id\n",
        "    num_train_epochs=1,                     # number of training epochs, use 3 at most\n",
        "    per_device_train_batch_size=3,          # batch size per device during training\n",
        "    gradient_accumulation_steps=2,          # number of steps before performing a backward/update pass\n",
        "    gradient_checkpointing=True,            # use gradient checkpointing to save memory, use in distributed training\n",
        "    gradient_checkpointing_kwargs={\"use_reentrant\": False}, # needed if gradient checkpoint is used\n",
        "    optim=\"adamw_torch_fused\",              # use fused adamw optimizer\n",
        "    logging_steps=100,                      # number of steps between two logs\n",
        "    save_strategy=\"epoch\",                  # save checkpoint every epoch\n",
        "    learning_rate=2e-4,                     # learning rate, based on QLoRA paper\n",
        "    bf16=True,                              # use bfloat16 precision\n",
        "    tf32=True,                              # use tf32 precision for better performance\n",
        "    max_grad_norm=0.3,                      # max gradient norm based on QLoRA paper\n",
        "    warmup_ratio=0.03,                      # warmup ratio based on QLoRA paper\n",
        "    lr_scheduler_type=\"constant\",           # use constant learning rate scheduler\n",
        "    push_to_hub=True,                       # push model to Hugging Face hub, optional\n",
        "    hub_model_id=\"starcoder2-3b-sft-qlora-cypher\", # if the model is pushed on HuggingFace\n",
        "    report_to=\"tensorboard\",                # report metrics to tensorboard, optional, if model is pushed to HuggingFace\n",
        ")"
      ],
      "metadata": {
        "id": "WuGTg65uVE5p"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @title SFTTrainer Parameters\n",
        "\n",
        "trainer = SFTTrainer(\n",
        "    model=model,\n",
        "    args=args,\n",
        "    train_dataset=train_dataset,\n",
        "    dataset_text_field=\"text\",\n",
        "    peft_config=peft_config,\n",
        "    max_seq_length=tokenizer.model_max_length,\n",
        "    tokenizer=tokenizer,\n",
        "    packing=True,\n",
        "    dataset_kwargs={\n",
        "        \"add_special_tokens\": False,  # the template adds the special tokens\n",
        "        \"append_concat_token\": False, # no need to add additional separator token\n",
        "    }\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "8d8e98ef9e9e432d9f58a909e866788d",
            "382e5ddecf014eea82023b05d34b95ed",
            "7a3943eb7deb4b12a6f536e105fe2084",
            "c1703985a42246898d6ce89a7561b392",
            "c5f6f61c298c47c19ddf0fbbb63bff7a",
            "3190d4c683a24f3c9f44b73c51a563ca",
            "1a8e2b1941f943f5b023d251b85cb29e",
            "093ad8048c0a4185812e9b21086f37a4",
            "b57445b6768b46c88a7a56a8b36e1b38",
            "6095a423244646368a87d0ffbf9533f6",
            "afaef92d2b9f412b97cf69ba5b99f4dd"
          ]
        },
        "id": "UlIdMps0W2uN",
        "outputId": "d9ffe5c3-1247-4d5e-80bf-5a60fa9ad277"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8d8e98ef9e9e432d9f58a909e866788d"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Train the Model\n",
        "\n",
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 308
        },
        "id": "Z-Y62sVQXPqg",
        "outputId": "8e15492f-1376-4012-ce05-da39aac9d79f"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The input hidden states seems to be silently casted in float32, this might be related to the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in torch.bfloat16.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='471' max='471' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [471/471 24:27, Epoch 1/1]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>0.570300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>200</td>\n",
              "      <td>0.152100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>300</td>\n",
              "      <td>0.098200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>400</td>\n",
              "      <td>0.073500</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=471, training_loss=0.19977091426808868, metrics={'train_runtime': 1471.5846, 'train_samples_per_second': 1.92, 'train_steps_per_second': 0.32, 'total_flos': 1.131939646930944e+17, 'train_loss': 0.19977091426808868, 'epoch': 1.0})"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Save Model Locally\n",
        "\n",
        "trainer.save_model()"
      ],
      "metadata": {
        "id": "qbbizLpRSMn8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8f55cdc4-1285-492b-9eb7-3778c4b24c19"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Clear Memory\n",
        "\n",
        "del trainer\n",
        "del model\n",
        "del tokenizer\n",
        "\n",
        "gc.collect()\n",
        "torch.cuda.empty_cache()"
      ],
      "metadata": {
        "id": "0sCYtYipL60e"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Basic Inference Tests"
      ],
      "metadata": {
        "id": "zFrKyazOTISN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Load Tokenizer & Model\n",
        "\n",
        "peft_model_id = \"solanaO/starcoder2-3b-sft-qlora-cypher\"  # if using HuggingFace model\n",
        "#peft_model_id = model_path  # if using the local model\n",
        "\n",
        "# Load Model with PEFT adapter\n",
        "model = AutoPeftModelForCausalLM.from_pretrained(\n",
        "  peft_model_id,\n",
        "  device_map=\"auto\",  # use auto for inference\n",
        "  torch_dtype=torch.float16\n",
        ")\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(peft_model_id)\n",
        "#model.resize_token_embeddings(len(tokenizer))\n",
        "\n",
        "# Text generation pipeline\n",
        "pipe = pipeline(\"text-generation\", model=model, tokenizer=tokenizer)"
      ],
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      "execution_count": 37,
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        {
          "output_type": "display_data",
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              "adapter_config.json:   0%|          | 0.00/703 [00:00<?, ?B/s]"
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              "tokenizer.json:   0%|          | 0.00/2.06M [00:00<?, ?B/s]"
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        {
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          "data": {
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              "adapter_model.safetensors:   0%|          | 0.00/763M [00:00<?, ?B/s]"
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The model 'PeftModelForCausalLM' is not supported for text-generation. Supported models are ['BartForCausalLM', 'BertLMHeadModel', 'BertGenerationDecoder', 'BigBirdForCausalLM', 'BigBirdPegasusForCausalLM', 'BioGptForCausalLM', 'BlenderbotForCausalLM', 'BlenderbotSmallForCausalLM', 'BloomForCausalLM', 'CamembertForCausalLM', 'LlamaForCausalLM', 'CodeGenForCausalLM', 'CohereForCausalLM', 'CpmAntForCausalLM', 'CTRLLMHeadModel', 'Data2VecTextForCausalLM', 'DbrxForCausalLM', 'ElectraForCausalLM', 'ErnieForCausalLM', 'FalconForCausalLM', 'FuyuForCausalLM', 'GemmaForCausalLM', 'GitForCausalLM', 'GPT2LMHeadModel', 'GPT2LMHeadModel', 'GPTBigCodeForCausalLM', 'GPTNeoForCausalLM', 'GPTNeoXForCausalLM', 'GPTNeoXJapaneseForCausalLM', 'GPTJForCausalLM', 'JambaForCausalLM', 'JetMoeForCausalLM', 'LlamaForCausalLM', 'MambaForCausalLM', 'MarianForCausalLM', 'MBartForCausalLM', 'MegaForCausalLM', 'MegatronBertForCausalLM', 'MistralForCausalLM', 'MixtralForCausalLM', 'MptForCausalLM', 'MusicgenForCausalLM', 'MusicgenMelodyForCausalLM', 'MvpForCausalLM', 'OlmoForCausalLM', 'OpenLlamaForCausalLM', 'OpenAIGPTLMHeadModel', 'OPTForCausalLM', 'PegasusForCausalLM', 'PersimmonForCausalLM', 'PhiForCausalLM', 'Phi3ForCausalLM', 'PLBartForCausalLM', 'ProphetNetForCausalLM', 'QDQBertLMHeadModel', 'Qwen2ForCausalLM', 'Qwen2MoeForCausalLM', 'RecurrentGemmaForCausalLM', 'ReformerModelWithLMHead', 'RemBertForCausalLM', 'RobertaForCausalLM', 'RobertaPreLayerNormForCausalLM', 'RoCBertForCausalLM', 'RoFormerForCausalLM', 'RwkvForCausalLM', 'Speech2Text2ForCausalLM', 'StableLmForCausalLM', 'Starcoder2ForCausalLM', 'TransfoXLLMHeadModel', 'TrOCRForCausalLM', 'WhisperForCausalLM', 'XGLMForCausalLM', 'XLMWithLMHeadModel', 'XLMProphetNetForCausalLM', 'XLMRobertaForCausalLM', 'XLMRobertaXLForCausalLM', 'XLNetLMHeadModel', 'XmodForCausalLM'].\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Load Test Dataset\n",
        "\n",
        "test_dataset = load_dataset(\"json\", data_files=data_path+\"test_dataset.json\", split=\"train\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
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        },
        "id": "hWCFF72sWV_Z",
        "outputId": "56ed8dd5-04c2-4e27-c25c-fd4c820ea20b"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f6a433b96d574ba58abfb2753e3c4dea"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title One Sample Test\n",
        "\n",
        "prompt = pipe.tokenizer.apply_chat_template(test_dataset[101][\"messages\"][:2],\n",
        "                                            tokenize=False,\n",
        "                                            add_generation_prompt=True)\n",
        "print(prompt)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a7YGZWSnMvg6",
        "outputId": "cf264fe8-d181-4c68-eb9d-f3af41ecab01"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<|im_start|>system\n",
            "\n",
            "You are a text to Cypher query translator. Convert the following question into a Cypher query using the provided graph schema!\n",
            "Graph schema: Relevant node labels and their properties (with datatypes) are:\n",
            "Author {affiliation: STRING}\n",
            "Author {first_name: STRING}\n",
            "<|im_end|><|endoftext|>\n",
            "<|im_start|>user\n",
            "Retrieve the Author where affiliation or first_name contains unspecified!<|im_end|><|endoftext|>\n",
            "<|im_start|>assistant\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Generate the output and print it\n",
        "\n",
        "outputs = pipe(prompt,\n",
        "              max_new_tokens=256,\n",
        "              do_sample=False,\n",
        "              temperature=0.1,\n",
        "              top_k=50,\n",
        "              top_p=0.1\n",
        "              )\n",
        "\n",
        "print(f\"Question: {test_dataset[101]['messages'][1]['content']}\")\n",
        "print(f\"Corect Cypher: {test_dataset[101]['messages'][2]['content']}\")\n",
        "print(f\"Generated Cypher: {outputs[0]['generated_text'][len(prompt):-10].strip()}\") # remove end generation token"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YH9Ca4zmTe2v",
        "outputId": "705617cb-3926-4e35-cfd4-53acb5324a9a"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Question: Retrieve the Author where affiliation or first_name contains unspecified!\n",
            "Corect Cypher: MATCH (n:Author) WHERE n.affiliation CONTAINS 'unspecified' RETURN n AS node UNION ALL MATCH (m:Author) WHERE m.first_name CONTAINS 'unspecified' RETURN m AS node\n",
            "Generated Cypher: MATCH (n:Author) WHERE n.affiliation CONTAINS 'unspecified' RETURN n AS node UNION ALL MATCH (m:Author) WHERE m.first_name CONTAINS 'unspecified' RETURN m AS node\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Test the Fine-Tuned Model"
      ],
      "metadata": {
        "id": "eruNIa5FJzXm"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "id": "qsOZotZqzLBx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8510a017-06ea-4ab7-dbe8-d4a9464bfb5e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 100/100 [05:25<00:00,  3.26s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 85.00%\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "#@title Test on a Subset of Samples\n",
        "from tqdm import tqdm\n",
        "\n",
        "# Compare the generated text with provided Cypher statement\n",
        "\n",
        "def evaluate(sample):\n",
        "    prompt = pipe.tokenizer.apply_chat_template(sample[\"messages\"][:2],\n",
        "                                                tokenize=False,\n",
        "                                                add_generation_prompt=True)\n",
        "    outputs = pipe(prompt,\n",
        "                   max_new_tokens=256,\n",
        "                   do_sample=True,\n",
        "                   temperature=0.7,\n",
        "                   top_k=50,\n",
        "                   top_p=0.95\n",
        "                   )\n",
        "\n",
        "    predicted_answer = outputs[0]['generated_text'][len(prompt):-10].strip() # remove end generation token\n",
        "\n",
        "    if predicted_answer == sample[\"messages\"][2][\"content\"]:\n",
        "        return 1\n",
        "    else:\n",
        "        return 0\n",
        "\n",
        "success_rate = []\n",
        "number_of_eval_samples = 100\n",
        "\n",
        "# Iterate over sample dataset and predict\n",
        "for s in tqdm(test_dataset.shuffle().select(range(number_of_eval_samples))):\n",
        "    success_rate.append(evaluate(s))\n",
        "\n",
        "# compute accuracy\n",
        "accuracy = sum(success_rate)/len(success_rate)\n",
        "\n",
        "print(f\"Accuracy: {accuracy*100:.2f}%\")"
      ]
    }
  ]
}